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  • Explainability of Agent-based Models as a Tool for Validation and Exploration

    Project ID: STAI-CDT-2023-KCL-11
    Themes: Argumentation, Verification
    Supervisor: Dr Steffen Zschaler, Dr Katie Bentley

    Agent-based models (ABMs) are an AI technique to help improve our understanding of complex real-world interactions and their “emergent behaviours”. ABMs are used to develop and test theories or to explore how interventions...

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  • Computational Social Choice and Machine Learning for Ethical Decision Making

    Project ID: STAI-CDT-2023-KCL-5
    Themes: AI Planning, Argumentation, Norms, Reasoning
    Supervisor: Dr Maria Polukarov

    The problem of ethical decision making presents a grand challenge for modern AI research. Arguably, the main obstacle to automating ethical decisions is the lack of a formal specification of ground-truth ethical principles,...

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  • Creating and evolving knowledge graphs at scale for explainable AI

    Project ID: STAI-CDT-2022-KCL-1
    Themes: AI Provenance, Argumentation, Verification
    Supervisor: Prof Elena Simperl

    Knowledge graphs and knowledge bases are forms of symbolic knowledge representations used across AI applications. Both refer to a set of technologies that organise data for easier access, capture information about people,...

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  • Argumentation-based Interactive Explainable Scheduling

    Project ID: STAI-CDT-2021-IC-11
    Themes: Argumentation
    Supervisor: Ruth Misener

    AI is continuing to make progress in many settings, fuelled by data availability and computational power, but it is widely acknowledged that it cannot fully benefit society without addressing its widespread inability to...

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